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Paper Citation Record · LEDGER

Investigating Length Issues in Document-level Machine Translation

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2412.17592.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.17592 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:27:33.472653Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T11:27:24.325729Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation be655c02-a53e-454e-a780-9cd6a8798237 · inbound

Optimising ChatGPT for creativity in literary translation: A case study from English into Dutch, Chinese, Catalan and Spanish cites this paper.

Optimising ChatGPT for creativity in literary translation: A case study from English into Dutch, Chinese, Catalan and Spanish Investigating Length Issues in Document-level Machine Translation

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-16T10:27:33.472653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T10:27:33.472653Z digest=sha256:98fbe72ac33ba02a54f4c52ebb746c539974b7536d2b19471b47d1c675df4359

Observation c626ea8c-3bc9-4a15-95e2-c5e17d3f6d10 · inbound

XToM: Exploring the Multilingual Theory of Mind for Large Language Models cites this paper.

XToM: Exploring the Multilingual Theory of Mind for Large Language Models Investigating Length Issues in Document-level Machine Translation

Reference 2022

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:27:24.330059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T11:27:24.267864Z digest=sha256:f5ca3ab8a7769c098d80b804dc113c3aa43f1db5cc8cf6fb8032790b42a6499a